Overview

Dataset statistics

Number of variables23
Number of observations792
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory142.4 KiB
Average record size in memory184.2 B

Variable types

Numeric21
Categorical2

Alerts

Año is highly overall correlated with Mbps (Media de bajada) and 2 other fieldsHigh correlation
Accesos por cada 100 hogares is highly overall correlated with Banda ancha fija and 9 other fieldsHigh correlation
Banda ancha fija is highly overall correlated with Accesos por cada 100 hogares and 14 other fieldsHigh correlation
Dial up is highly overall correlated with Entre 1 Mbps y 6 MbpsHigh correlation
Total_BA_P is highly overall correlated with Accesos por cada 100 hogares and 13 other fieldsHigh correlation
ADSL is highly overall correlated with Banda ancha fija and 6 other fieldsHigh correlation
Cablemodem is highly overall correlated with Accesos por cada 100 hogares and 12 other fieldsHigh correlation
Fibra óptica is highly overall correlated with Accesos por cada 100 hogares and 11 other fieldsHigh correlation
Wireless is highly overall correlated with Accesos por cada 100 hogares and 8 other fieldsHigh correlation
Otros_tecno_P is highly overall correlated with Accesos por cada 100 hogares and 7 other fieldsHigh correlation
Total_tecno_P is highly overall correlated with Accesos por cada 100 hogares and 13 other fieldsHigh correlation
Mbps (Media de bajada) is highly overall correlated with Año and 5 other fieldsHigh correlation
Entre 1 Mbps y 6 Mbps is highly overall correlated with Banda ancha fija and 8 other fieldsHigh correlation
Entre 6 Mbps y 10 Mbps is highly overall correlated with Banda ancha fija and 7 other fieldsHigh correlation
Entre 10 Mbps y 20 Mbps is highly overall correlated with Banda ancha fija and 4 other fieldsHigh correlation
Más de 30 Mbps is highly overall correlated with Año and 11 other fieldsHigh correlation
Otros_velo_rango_P is highly overall correlated with Año and 2 other fieldsHigh correlation
Total_velo_rango_P is highly overall correlated with Accesos por cada 100 hogares and 13 other fieldsHigh correlation
Provincia is highly overall correlated with Accesos por cada 100 hogares and 4 other fieldsHigh correlation
Provincia is uniformly distributedUniform
Dial up has 59 (7.4%) zerosZeros
Cablemodem has 14 (1.8%) zerosZeros
Fibra óptica has 9 (1.1%) zerosZeros
Wireless has 38 (4.8%) zerosZeros
Entre 512 Kbps y 1 Mbps has 50 (6.3%) zerosZeros
Entre 6 Mbps y 10 Mbps has 38 (4.8%) zerosZeros
Entre 10 Mbps y 20 Mbps has 71 (9.0%) zerosZeros
Entre 20 Mbps y 30 Mbps has 104 (13.1%) zerosZeros
Más de 30 Mbps has 112 (14.1%) zerosZeros
Otros_velo_rango_P has 449 (56.7%) zerosZeros

Reproduction

Analysis started2023-07-17 16:54:06.260881
Analysis finished2023-07-17 16:55:29.332429
Duration1 minute and 23.07 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Año
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6364
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:29.415865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2021
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3860299
Coefficient of variation (CV)0.0011825867
Kurtosis-1.1859202
Mean2017.6364
Median Absolute Deviation (MAD)2
Skewness0.031978903
Sum1597968
Variance5.6931387
MonotonicityDecreasing
2023-07-17T11:55:29.573381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 96
12.1%
2020 96
12.1%
2019 96
12.1%
2018 96
12.1%
2017 96
12.1%
2016 96
12.1%
2015 96
12.1%
2014 96
12.1%
2022 24
 
3.0%
ValueCountFrequency (%)
2014 96
12.1%
2015 96
12.1%
2016 96
12.1%
2017 96
12.1%
2018 96
12.1%
2019 96
12.1%
2020 96
12.1%
2021 96
12.1%
2022 24
 
3.0%
ValueCountFrequency (%)
2022 24
 
3.0%
2021 96
12.1%
2020 96
12.1%
2019 96
12.1%
2018 96
12.1%
2017 96
12.1%
2016 96
12.1%
2015 96
12.1%
2014 96
12.1%

Trimestre
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
1
216 
4
192 
3
192 
2
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters792
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Length

2023-07-17T11:55:29.747805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T11:55:29.951005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring characters

ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Provincia
Categorical

HIGH CORRELATION  UNIFORM 

Distinct24
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Buenos Aires
 
33
Capital Federal
 
33
Tierra Del Fuego
 
33
Santiago Del Estero
 
33
Santa Fe
 
33
Other values (19)
627 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters7062
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 33
 
4.2%
Capital Federal 33
 
4.2%
Tierra Del Fuego 33
 
4.2%
Santiago Del Estero 33
 
4.2%
Santa Fe 33
 
4.2%
Santa Cruz 33
 
4.2%
San Luis 33
 
4.2%
San Juan 33
 
4.2%
Salta 33
 
4.2%
Río Negro 33
 
4.2%
Other values (14) 462
58.3%

Length

2023-07-17T11:55:30.126052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 66
 
5.3%
la 66
 
5.3%
del 66
 
5.3%
san 66
 
5.3%
entre 33
 
2.6%
rioja 33
 
2.6%
pampa 33
 
2.6%
jujuy 33
 
2.6%
formosa 33
 
2.6%
buenos 33
 
2.6%
Other values (24) 792
63.2%

Most occurring characters

ValueCountFrequency (%)
a 924
 
13.1%
e 561
 
7.9%
o 495
 
7.0%
462
 
6.5%
n 429
 
6.1%
u 429
 
6.1%
r 429
 
6.1%
t 330
 
4.7%
s 297
 
4.2%
i 297
 
4.2%
Other values (30) 2409
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5346
75.7%
Uppercase Letter 1254
 
17.8%
Space Separator 462
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 924
17.3%
e 561
10.5%
o 495
9.3%
n 429
8.0%
u 429
8.0%
r 429
8.0%
t 330
 
6.2%
s 297
 
5.6%
i 297
 
5.6%
l 165
 
3.1%
Other values (15) 990
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 231
18.4%
S 198
15.8%
F 132
10.5%
L 99
7.9%
R 99
7.9%
N 66
 
5.3%
M 66
 
5.3%
J 66
 
5.3%
E 66
 
5.3%
D 66
 
5.3%
Other values (4) 165
13.2%
Space Separator
ValueCountFrequency (%)
462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6600
93.5%
Common 462
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 924
14.0%
e 561
 
8.5%
o 495
 
7.5%
n 429
 
6.5%
u 429
 
6.5%
r 429
 
6.5%
t 330
 
5.0%
s 297
 
4.5%
i 297
 
4.5%
C 231
 
3.5%
Other values (29) 2178
33.0%
Common
ValueCountFrequency (%)
462
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6897
97.7%
None 165
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 924
13.4%
e 561
 
8.1%
o 495
 
7.2%
462
 
6.7%
n 429
 
6.2%
u 429
 
6.2%
r 429
 
6.2%
t 330
 
4.8%
s 297
 
4.3%
i 297
 
4.3%
Other values (26) 2244
32.5%
None
ValueCountFrequency (%)
í 66
40.0%
ó 33
20.0%
é 33
20.0%
á 33
20.0%
Distinct733
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.907184
Minimum9.35
Maximum124.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:30.316829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.35
5-th percentile17.0655
Q131.54
median44.625
Q360.575
95-th percentile93.0445
Maximum124.06
Range114.71
Interquartile range (IQR)29.035

Descriptive statistics

Standard deviation22.810661
Coefficient of variation (CV)0.4761428
Kurtosis0.9392401
Mean47.907184
Median Absolute Deviation (MAD)14.145
Skewness0.938509
Sum37942.49
Variance520.32624
MonotonicityNot monotonic
2023-07-17T11:55:30.524703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.49 5
 
0.6%
9.71 4
 
0.5%
46.39 3
 
0.4%
28.91 3
 
0.4%
39.15 2
 
0.3%
55.63 2
 
0.3%
60.44 2
 
0.3%
16.7 2
 
0.3%
45.33 2
 
0.3%
9.72 2
 
0.3%
Other values (723) 765
96.6%
ValueCountFrequency (%)
9.35 1
 
0.1%
9.51 1
 
0.1%
9.55 1
 
0.1%
9.59 1
 
0.1%
9.6 1
 
0.1%
9.63 1
 
0.1%
9.64 1
 
0.1%
9.71 4
0.5%
9.72 2
0.3%
9.79 1
 
0.1%
ValueCountFrequency (%)
124.06 1
0.1%
122.9 1
0.1%
121.86 1
0.1%
120.77 1
0.1%
120.63 1
0.1%
120.04 1
0.1%
118.29 1
0.1%
118.13 1
0.1%
116.07 1
0.1%
115.16 1
0.1%

Banda ancha fija
Real number (ℝ)

Distinct785
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337853.1
Minimum12193
Maximum4549131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:30.734790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12193
5-th percentile25361.95
Q151371.5
median100193.5
Q3171970.5
95-th percentile1410772.9
Maximum4549131
Range4536938
Interquartile range (IQR)120599

Descriptive statistics

Standard deviation724801.35
Coefficient of variation (CV)2.145315
Kurtosis13.992619
Mean337853.1
Median Absolute Deviation (MAD)54730
Skewness3.691903
Sum2.6757966 × 108
Variance5.25337 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:30.933272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12992 3
 
0.4%
31938 3
 
0.4%
169274 2
 
0.3%
33970 2
 
0.3%
26304 2
 
0.3%
106934 1
 
0.1%
51301 1
 
0.1%
26094 1
 
0.1%
561470 1
 
0.1%
35049 1
 
0.1%
Other values (775) 775
97.9%
ValueCountFrequency (%)
12193 1
0.1%
12683 1
0.1%
12691 1
0.1%
12788 1
0.1%
12878 1
0.1%
12901 1
0.1%
12902 1
0.1%
12904 1
0.1%
12915 1
0.1%
12939 1
0.1%
ValueCountFrequency (%)
4549131 1
0.1%
4502772 1
0.1%
4246425 1
0.1%
4127167 1
0.1%
4054936 1
0.1%
4028195 1
0.1%
3950114 1
0.1%
3932211 1
0.1%
3845594 1
0.1%
3772480 1
0.1%

Dial up
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct297
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1001.9508
Minimum0
Maximum15229
Zeros59
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:31.131300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.75
median148
Q3620
95-th percentile6688
Maximum15229
Range15229
Interquartile range (IQR)610.25

Descriptive statistics

Standard deviation2468.1174
Coefficient of variation (CV)2.4633121
Kurtosis13.333028
Mean1001.9508
Median Absolute Deviation (MAD)147
Skewness3.6720425
Sum793545
Variance6091603.6
MonotonicityNot monotonic
2023-07-17T11:55:31.329302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
7.4%
1 48
 
6.1%
3 36
 
4.5%
2 31
 
3.9%
13 19
 
2.4%
18 14
 
1.8%
619 11
 
1.4%
12 10
 
1.3%
148 8
 
1.0%
9 8
 
1.0%
Other values (287) 548
69.2%
ValueCountFrequency (%)
0 59
7.4%
1 48
6.1%
2 31
3.9%
3 36
4.5%
4 4
 
0.5%
5 3
 
0.4%
6 1
 
0.1%
7 7
 
0.9%
8 1
 
0.1%
9 8
 
1.0%
ValueCountFrequency (%)
15229 1
0.1%
14766 1
0.1%
14144 1
0.1%
14116 1
0.1%
14028 1
0.1%
12711 1
0.1%
12695 1
0.1%
12642 1
0.1%
12612 1
0.1%
12515 1
0.1%

Total_BA_P
Real number (ℝ)

Distinct783
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338855.06
Minimum12557
Maximum4555424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:31.545846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12557
5-th percentile25551.85
Q151879
median100660
Q3172002.5
95-th percentile1412697.7
Maximum4555424
Range4542867
Interquartile range (IQR)120123.5

Descriptive statistics

Standard deviation726760.45
Coefficient of variation (CV)2.1447532
Kurtosis13.96188
Mean338855.06
Median Absolute Deviation (MAD)54845.5
Skewness3.6894897
Sum2.683732 × 108
Variance5.2818075 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:31.746063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13307 3
 
0.4%
32557 3
 
0.4%
13232 2
 
0.3%
170823 2
 
0.3%
117903 2
 
0.3%
34405 2
 
0.3%
51968 2
 
0.3%
16930 1
 
0.1%
70364 1
 
0.1%
26464 1
 
0.1%
Other values (773) 773
97.6%
ValueCountFrequency (%)
12557 1
0.1%
13046 1
0.1%
13055 1
0.1%
13147 1
0.1%
13218 1
0.1%
13219 1
0.1%
13221 1
0.1%
13232 2
0.3%
13256 1
0.1%
13293 1
0.1%
ValueCountFrequency (%)
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3960233 1
0.1%
3937277 1
0.1%
3855698 1
0.1%
3777546 1
0.1%

ADSL
Real number (ℝ)

Distinct707
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131682.47
Minimum7987
Maximum1586343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:31.946253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7987
5-th percentile11355.95
Q122636
median48884
Q3107801.25
95-th percentile451784.3
Maximum1586343
Range1578356
Interquartile range (IQR)85165.25

Descriptive statistics

Standard deviation261414.97
Coefficient of variation (CV)1.9851919
Kurtosis18.635125
Mean131682.47
Median Absolute Deviation (MAD)29023
Skewness4.1714874
Sum1.0429251 × 108
Variance6.8337788 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:32.157179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22636 9
 
1.1%
48670 9
 
1.1%
11432 9
 
1.1%
12754 5
 
0.6%
40072 4
 
0.5%
8417 4
 
0.5%
12347 4
 
0.5%
12853 4
 
0.5%
40092 3
 
0.4%
12845 3
 
0.4%
Other values (697) 738
93.2%
ValueCountFrequency (%)
7987 1
 
0.1%
8003 1
 
0.1%
8175 1
 
0.1%
8301 1
 
0.1%
8401 2
0.3%
8417 4
0.5%
8472 1
 
0.1%
8551 1
 
0.1%
8553 1
 
0.1%
8569 1
 
0.1%
ValueCountFrequency (%)
1586343 1
0.1%
1585467 1
0.1%
1583560 1
0.1%
1583135 1
0.1%
1581770 1
0.1%
1579448 1
0.1%
1575978 1
0.1%
1574216 1
0.1%
1568881 1
0.1%
1567685 1
0.1%

Cablemodem
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct681
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167020.02
Minimum0
Maximum2728865
Zeros14
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:32.375508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile451
Q17784
median34058.5
Q368596.75
95-th percentile1086449.5
Maximum2728865
Range2728865
Interquartile range (IQR)60812.75

Descriptive statistics

Standard deviation410433.91
Coefficient of variation (CV)2.4573935
Kurtosis15.051295
Mean167020.02
Median Absolute Deviation (MAD)27372
Skewness3.7441312
Sum1.3227986 × 108
Variance1.6845599 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:32.568739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34984 15
 
1.9%
451 14
 
1.8%
0 14
 
1.8%
13030 13
 
1.6%
3900 7
 
0.9%
2722 5
 
0.6%
4365 4
 
0.5%
1025 4
 
0.5%
242 3
 
0.4%
5068 3
 
0.4%
Other values (671) 710
89.6%
ValueCountFrequency (%)
0 14
1.8%
46 1
 
0.1%
83 1
 
0.1%
97 3
 
0.4%
100 1
 
0.1%
115 1
 
0.1%
241 2
 
0.3%
242 3
 
0.4%
243 2
 
0.3%
244 1
 
0.1%
ValueCountFrequency (%)
2728865 1
0.1%
2706506 1
0.1%
2595485 1
0.1%
2503830 1
0.1%
2452056 1
0.1%
2441248 1
0.1%
2384557 1
0.1%
2244277 1
0.1%
2210349 1
0.1%
2191672 1
0.1%

Fibra óptica
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct550
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24660.528
Minimum0
Maximum1242121
Zeros9
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:32.770227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1127
median998
Q310793.25
95-th percentile112058.55
Maximum1242121
Range1242121
Interquartile range (IQR)10666.25

Descriptive statistics

Standard deviation104306.1
Coefficient of variation (CV)4.2296782
Kurtosis69.480392
Mean24660.528
Median Absolute Deviation (MAD)982.5
Skewness7.8736253
Sum19531138
Variance1.0879762 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:32.988230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 17
 
2.1%
116 13
 
1.6%
16 13
 
1.6%
15 12
 
1.5%
22 12
 
1.5%
354 11
 
1.4%
14 10
 
1.3%
916 10
 
1.3%
0 9
 
1.1%
665 9
 
1.1%
Other values (540) 676
85.4%
ValueCountFrequency (%)
0 9
1.1%
1 6
0.8%
2 2
 
0.3%
4 4
0.5%
5 2
 
0.3%
6 8
1.0%
9 1
 
0.1%
11 3
 
0.4%
12 3
 
0.4%
13 5
0.6%
ValueCountFrequency (%)
1242121 1
0.1%
1176024 1
0.1%
885613 1
0.1%
854173 1
0.1%
821597 1
0.1%
804991 1
0.1%
749087 1
0.1%
723072 1
0.1%
720688 1
0.1%
669194 1
0.1%

Wireless
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct559
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9566.2222
Minimum0
Maximum126887
Zeros38
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:33.190344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1251
median3936.5
Q311897.25
95-th percentile46659.15
Maximum126887
Range126887
Interquartile range (IQR)11646.25

Descriptive statistics

Standard deviation17114.841
Coefficient of variation (CV)1.7890909
Kurtosis14.690973
Mean9566.2222
Median Absolute Deviation (MAD)3895.5
Skewness3.5088686
Sum7576448
Variance2.9291778 × 108
MonotonicityNot monotonic
2023-07-17T11:55:33.404559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.8%
1 36
 
4.5%
5 13
 
1.6%
2 11
 
1.4%
52 8
 
1.0%
1354 7
 
0.9%
1023 6
 
0.8%
911 6
 
0.8%
253 5
 
0.6%
252 5
 
0.6%
Other values (549) 657
83.0%
ValueCountFrequency (%)
0 38
4.8%
1 36
4.5%
2 11
 
1.4%
3 2
 
0.3%
5 13
 
1.6%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
13 4
 
0.5%
14 4
 
0.5%
ValueCountFrequency (%)
126887 1
0.1%
125521 1
0.1%
120228 1
0.1%
113546 1
0.1%
98806 1
0.1%
94162 1
0.1%
93444 1
0.1%
91736 1
0.1%
87111 1
0.1%
85572 1
0.1%

Otros_tecno_P
Real number (ℝ)

Distinct530
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6400.1149
Minimum13
Maximum73415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:33.609008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile151.65
Q1637.5
median2685
Q37377.5
95-th percentile28641.25
Maximum73415
Range73402
Interquartile range (IQR)6740

Descriptive statistics

Standard deviation9766.4951
Coefficient of variation (CV)1.5259875
Kurtosis11.246938
Mean6400.1149
Median Absolute Deviation (MAD)2335
Skewness2.9778395
Sum5068891
Variance95384427
MonotonicityNot monotonic
2023-07-17T11:55:33.813256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
340 15
 
1.9%
350 14
 
1.8%
410 11
 
1.4%
300 11
 
1.4%
280 8
 
1.0%
235 8
 
1.0%
150 7
 
0.9%
430 6
 
0.8%
310 6
 
0.8%
80 6
 
0.8%
Other values (520) 700
88.4%
ValueCountFrequency (%)
13 1
 
0.1%
20 1
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
70 1
 
0.1%
80 6
0.8%
90 4
0.5%
101 1
 
0.1%
110 1
 
0.1%
113 2
 
0.3%
ValueCountFrequency (%)
73415 1
0.1%
66872 1
0.1%
64554 1
0.1%
57927 1
0.1%
57864 1
0.1%
57547 1
0.1%
57189 1
0.1%
55541 1
0.1%
43622 1
0.1%
43608 1
0.1%

Total_tecno_P
Real number (ℝ)

Distinct783
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338855.06
Minimum12557
Maximum4555424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:34.039224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12557
5-th percentile25551.85
Q151879
median100660
Q3172002.5
95-th percentile1412697.7
Maximum4555424
Range4542867
Interquartile range (IQR)120123.5

Descriptive statistics

Standard deviation726760.45
Coefficient of variation (CV)2.1447532
Kurtosis13.96188
Mean338855.06
Median Absolute Deviation (MAD)54845.5
Skewness3.6894897
Sum2.683732 × 108
Variance5.2818075 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:34.236389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13307 3
 
0.4%
32557 3
 
0.4%
13232 2
 
0.3%
170823 2
 
0.3%
117903 2
 
0.3%
34405 2
 
0.3%
51968 2
 
0.3%
16930 1
 
0.1%
70364 1
 
0.1%
26464 1
 
0.1%
Other values (773) 773
97.6%
ValueCountFrequency (%)
12557 1
0.1%
13046 1
0.1%
13055 1
0.1%
13147 1
0.1%
13218 1
0.1%
13219 1
0.1%
13221 1
0.1%
13232 2
0.3%
13256 1
0.1%
13293 1
0.1%
ValueCountFrequency (%)
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3960233 1
0.1%
3937277 1
0.1%
3855698 1
0.1%
3777546 1
0.1%

Mbps (Media de bajada)
Real number (ℝ)

Distinct60
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.352273
Minimum3
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:34.439556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median6.5
Q315
95-th percentile39
Maximum88
Range85
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.83685
Coefficient of variation (CV)1.0392298
Kurtosis5.8306338
Mean12.352273
Median Absolute Deviation (MAD)3.5
Skewness2.2110624
Sum9783
Variance164.78472
MonotonicityNot monotonic
2023-07-17T11:55:34.638827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 115
14.5%
4 114
14.4%
6 87
 
11.0%
5 80
 
10.1%
7 40
 
5.1%
8 34
 
4.3%
9 25
 
3.2%
12 22
 
2.8%
13 21
 
2.7%
11 21
 
2.7%
Other values (50) 233
29.4%
ValueCountFrequency (%)
3 115
14.5%
4 114
14.4%
5 80
10.1%
6 87
11.0%
7 40
 
5.1%
8 34
 
4.3%
9 25
 
3.2%
10 19
 
2.4%
11 21
 
2.7%
12 22
 
2.8%
ValueCountFrequency (%)
88 1
0.1%
83 1
0.1%
78 1
0.1%
73 1
0.1%
68 1
0.1%
67 1
0.1%
64 1
0.1%
63 1
0.1%
60 2
0.3%
59 1
0.1%

Hasta 512 kbps
Real number (ℝ)

Distinct366
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125923.96
Minimum1007
Maximum998000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:34.843305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile1526.95
Q19364.5
median48000
Q3135500
95-th percentile568000
Maximum998000
Range996993
Interquartile range (IQR)126135.5

Descriptive statistics

Standard deviation190102.09
Coefficient of variation (CV)1.5096578
Kurtosis5.396598
Mean125923.96
Median Absolute Deviation (MAD)43489
Skewness2.2997132
Sum99731774
Variance3.6138803 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:35.055356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 20
 
2.5%
15000 16
 
2.0%
10000 15
 
1.9%
16000 15
 
1.9%
18000 14
 
1.8%
8000 12
 
1.5%
67000 12
 
1.5%
26000 11
 
1.4%
6000 11
 
1.4%
71000 10
 
1.3%
Other values (356) 656
82.8%
ValueCountFrequency (%)
1007 1
 
0.1%
1009 1
 
0.1%
1010 3
 
0.4%
1011 1
 
0.1%
1053 1
 
0.1%
1058 1
 
0.1%
1063 1
 
0.1%
1107 1
 
0.1%
1110 8
1.0%
1119 1
 
0.1%
ValueCountFrequency (%)
998000 1
 
0.1%
991000 1
 
0.1%
986000 1
 
0.1%
973000 1
 
0.1%
959000 2
0.3%
958000 3
0.4%
852000 1
 
0.1%
847000 1
 
0.1%
840000 1
 
0.1%
791000 1
 
0.1%

Entre 512 Kbps y 1 Mbps
Real number (ℝ)

Distinct596
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97393.794
Minimum0
Maximum995000
Zeros50
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:35.263494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13491.5
median8894.5
Q384000
95-th percentile554000
Maximum995000
Range995000
Interquartile range (IQR)80508.5

Descriptive statistics

Standard deviation198510.95
Coefficient of variation (CV)2.03823
Kurtosis7.1758007
Mean97393.794
Median Absolute Deviation (MAD)7584.5
Skewness2.7260485
Sum77135885
Variance3.9406597 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:35.465882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
6.3%
1000 12
 
1.5%
4000 10
 
1.3%
285000 8
 
1.0%
97000 8
 
1.0%
327000 7
 
0.9%
109000 7
 
0.9%
909000 6
 
0.8%
112000 6
 
0.8%
466000 5
 
0.6%
Other values (586) 673
85.0%
ValueCountFrequency (%)
0 50
6.3%
1000 12
 
1.5%
1027 1
 
0.1%
1058 1
 
0.1%
1062 1
 
0.1%
1077 1
 
0.1%
1099 1
 
0.1%
1123 1
 
0.1%
1164 1
 
0.1%
1169 1
 
0.1%
ValueCountFrequency (%)
995000 1
 
0.1%
987000 1
 
0.1%
974000 1
 
0.1%
940000 2
 
0.3%
928000 2
 
0.3%
909000 6
0.8%
908000 1
 
0.1%
900000 1
 
0.1%
896000 1
 
0.1%
894000 1
 
0.1%

Entre 1 Mbps y 6 Mbps
Real number (ℝ)

Distinct783
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157162.09
Minimum3576
Maximum2299705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:35.671483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3576
5-th percentile12268.3
Q129264.75
median51598
Q387976
95-th percentile863543.05
Maximum2299705
Range2296129
Interquartile range (IQR)58711.25

Descriptive statistics

Standard deviation357296.3
Coefficient of variation (CV)2.2734255
Kurtosis19.748457
Mean157162.09
Median Absolute Deviation (MAD)28031.5
Skewness4.3265613
Sum1.2447237 × 108
Variance1.2766065 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:35.869441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14014 3
 
0.4%
35409 3
 
0.4%
22409 2
 
0.3%
58588 2
 
0.3%
30727 2
 
0.3%
40285 2
 
0.3%
28600 2
 
0.3%
415075 1
 
0.1%
22246 1
 
0.1%
87994 1
 
0.1%
Other values (773) 773
97.6%
ValueCountFrequency (%)
3576 1
0.1%
4386 1
0.1%
5018 1
0.1%
5312 1
0.1%
5366 1
0.1%
6038 1
0.1%
6755 1
0.1%
7219 1
0.1%
7423 1
0.1%
8276 1
0.1%
ValueCountFrequency (%)
2299705 1
0.1%
2288772 1
0.1%
2281524 1
0.1%
2279875 1
0.1%
2267852 1
0.1%
2266948 1
0.1%
2253197 1
0.1%
2250898 1
0.1%
2250445 1
0.1%
2214760 1
0.1%

Entre 6 Mbps y 10 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct709
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72779.756
Minimum0
Maximum917000
Zeros38
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:36.065521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q15186.75
median19966
Q363170.25
95-th percentile325915.8
Maximum917000
Range917000
Interquartile range (IQR)57983.5

Descriptive statistics

Standard deviation143431.38
Coefficient of variation (CV)1.9707593
Kurtosis12.961731
Mean72779.756
Median Absolute Deviation (MAD)16770
Skewness3.4420732
Sum57641567
Variance2.0572561 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:36.262321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.8%
2000 12
 
1.5%
1000 4
 
0.5%
11000 4
 
0.5%
655000 3
 
0.4%
15000 3
 
0.4%
1337 2
 
0.3%
2867 2
 
0.3%
14101 2
 
0.3%
31000 2
 
0.3%
Other values (699) 720
90.9%
ValueCountFrequency (%)
0 38
4.8%
1000 4
 
0.5%
1034 1
 
0.1%
1066 1
 
0.1%
1133 1
 
0.1%
1165 1
 
0.1%
1227 1
 
0.1%
1311 1
 
0.1%
1314 1
 
0.1%
1321 1
 
0.1%
ValueCountFrequency (%)
917000 1
0.1%
902000 1
0.1%
858000 1
0.1%
855000 1
0.1%
849000 1
0.1%
792000 1
0.1%
784000 1
0.1%
779000 1
0.1%
778000 1
0.1%
775000 1
0.1%

Entre 10 Mbps y 20 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct678
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82715.126
Minimum0
Maximum978000
Zeros71
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:36.459475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14582.25
median14852
Q356480
95-th percentile524197
Maximum978000
Range978000
Interquartile range (IQR)51897.75

Descriptive statistics

Standard deviation174830.17
Coefficient of variation (CV)2.1136421
Kurtosis9.4745775
Mean82715.126
Median Absolute Deviation (MAD)12692.5
Skewness3.0747897
Sum65510380
Variance3.056559 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:36.698317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
9.0%
1000 5
 
0.6%
5000 4
 
0.5%
119000 3
 
0.4%
10000 3
 
0.4%
100000 3
 
0.4%
531000 2
 
0.3%
641000 2
 
0.3%
111000 2
 
0.3%
22910 2
 
0.3%
Other values (668) 695
87.8%
ValueCountFrequency (%)
0 71
9.0%
1000 5
 
0.6%
1061 1
 
0.1%
1062 1
 
0.1%
1076 1
 
0.1%
1085 1
 
0.1%
1162 1
 
0.1%
1172 1
 
0.1%
1202 1
 
0.1%
1203 1
 
0.1%
ValueCountFrequency (%)
978000 1
0.1%
966000 1
0.1%
965000 1
0.1%
958000 1
0.1%
956000 1
0.1%
920000 1
0.1%
888000 1
0.1%
886678 1
0.1%
878000 1
0.1%
832000 1
0.1%

Entre 20 Mbps y 30 Mbps
Real number (ℝ)

Distinct540
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100582.82
Minimum0
Maximum997000
Zeros104
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:36.904236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12000
median10402
Q365540.25
95-th percentile623000
Maximum997000
Range997000
Interquartile range (IQR)63540.25

Descriptive statistics

Standard deviation207784.23
Coefficient of variation (CV)2.0658025
Kurtosis6.1517716
Mean100582.82
Median Absolute Deviation (MAD)10402
Skewness2.6103235
Sum79661590
Variance4.3174286 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:37.107435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
13.1%
1000 24
 
3.0%
5000 18
 
2.3%
2000 13
 
1.6%
3000 8
 
1.0%
4000 7
 
0.9%
22000 5
 
0.6%
29000 5
 
0.6%
17000 4
 
0.5%
41000 4
 
0.5%
Other values (530) 600
75.8%
ValueCountFrequency (%)
0 104
13.1%
1000 24
 
3.0%
1032 1
 
0.1%
1033 1
 
0.1%
1068 1
 
0.1%
1073 1
 
0.1%
1084 1
 
0.1%
1091 1
 
0.1%
1135 1
 
0.1%
1136 1
 
0.1%
ValueCountFrequency (%)
997000 1
0.1%
991000 1
0.1%
979000 1
0.1%
977000 1
0.1%
969000 1
0.1%
964000 1
0.1%
961000 1
0.1%
949093 1
0.1%
941000 1
0.1%
899000 1
0.1%

Más de 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct503
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66633.418
Minimum0
Maximum3381049
Zeros112
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:37.317089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median618.5
Q313928.25
95-th percentile248442.2
Maximum3381049
Range3381049
Interquartile range (IQR)13924.25

Descriptive statistics

Standard deviation298076.55
Coefficient of variation (CV)4.4733793
Kurtosis58.297456
Mean66633.418
Median Absolute Deviation (MAD)618.5
Skewness7.1200483
Sum52773667
Variance8.8849631 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:37.527451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
14.1%
2 39
 
4.9%
1 19
 
2.4%
3 15
 
1.9%
4 14
 
1.8%
10 13
 
1.6%
5 9
 
1.1%
22 8
 
1.0%
9 7
 
0.9%
7 6
 
0.8%
Other values (493) 550
69.4%
ValueCountFrequency (%)
0 112
14.1%
1 19
 
2.4%
2 39
 
4.9%
3 15
 
1.9%
4 14
 
1.8%
5 9
 
1.1%
6 1
 
0.1%
7 6
 
0.8%
8 5
 
0.6%
9 7
 
0.9%
ValueCountFrequency (%)
3381049 1
0.1%
3259793 1
0.1%
2482266 1
0.1%
2337604 1
0.1%
2246313 1
0.1%
2176242 1
0.1%
2085815 1
0.1%
1894466 1
0.1%
1802583 1
0.1%
1465133 1
0.1%

Otros_velo_rango_P
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)32.4%
Missing6
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean26008.611
Minimum-1945
Maximum898000
Zeros449
Zeros (%)56.7%
Negative2
Negative (%)0.3%
Memory size6.3 KiB
2023-07-17T11:55:37.739722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1945
5-th percentile0
Q10
median0
Q36433
95-th percentile91500
Maximum898000
Range899945
Interquartile range (IQR)6433

Descriptive statistics

Standard deviation113774.69
Coefficient of variation (CV)4.3745008
Kurtosis33.984303
Mean26008.611
Median Absolute Deviation (MAD)0
Skewness5.792076
Sum20442768
Variance1.294468 × 1010
MonotonicityNot monotonic
2023-07-17T11:55:37.938725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 449
56.7%
2151 8
 
1.0%
1035 6
 
0.8%
6105 6
 
0.8%
698000 4
 
0.5%
3719 4
 
0.5%
4500 4
 
0.5%
1618 4
 
0.5%
100000 4
 
0.5%
4779 3
 
0.4%
Other values (245) 294
37.1%
(Missing) 6
 
0.8%
ValueCountFrequency (%)
-1945 1
 
0.1%
-1000 1
 
0.1%
0 449
56.7%
1000 2
 
0.3%
1035 6
 
0.8%
1123 1
 
0.1%
1305 2
 
0.3%
1313 1
 
0.1%
1483 1
 
0.1%
1492 1
 
0.1%
ValueCountFrequency (%)
898000 1
 
0.1%
895000 1
 
0.1%
833000 1
 
0.1%
803000 1
 
0.1%
793000 1
 
0.1%
792000 1
 
0.1%
758000 1
 
0.1%
735000 2
0.3%
698000 4
0.5%
687000 1
 
0.1%

Total_velo_rango_P
Real number (ℝ)

Distinct787
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336995.94
Minimum12406
Maximum4555424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-07-17T11:55:38.142733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12406
5-th percentile25603.3
Q151852
median100876.5
Q3175246
95-th percentile1412697.7
Maximum4555424
Range4543018
Interquartile range (IQR)123394

Descriptive statistics

Standard deviation721714.88
Coefficient of variation (CV)2.141613
Kurtosis14.229921
Mean336995.94
Median Absolute Deviation (MAD)55244.5
Skewness3.7153506
Sum2.6690078 × 108
Variance5.2087237 × 1011
MonotonicityNot monotonic
2023-07-17T11:55:38.338370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14029 3
 
0.4%
35710 3
 
0.4%
33772 2
 
0.3%
4555424 1
 
0.1%
106947 1
 
0.1%
32013 1
 
0.1%
561542 1
 
0.1%
35054 1
 
0.1%
151631 1
 
0.1%
3121140 1
 
0.1%
Other values (777) 777
98.1%
ValueCountFrequency (%)
12406 1
0.1%
12557 1
0.1%
12741 1
0.1%
13040 1
0.1%
13055 1
0.1%
13147 1
0.1%
13220 1
0.1%
13302 1
0.1%
13488 1
0.1%
13660 1
0.1%
ValueCountFrequency (%)
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3971683 1
0.1%
3937277 1
0.1%
3870807 1
0.1%
3777546 1
0.1%

Interactions

2023-07-17T11:55:25.016243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:07.559916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:12.646458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:16.561780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:20.614197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:25.145619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.082298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:32.840434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:36.698041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:40.308415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.953936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:47.740964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.530766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.043550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:58.688053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.283904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.203920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:09.850074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:13.524265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.240552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.087653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:25.198131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:07.789662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:12.839009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:16.738131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:20.824937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:25.337666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.281602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.032565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:36.886938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:40.517957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:44.149140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:47.925864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.714769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.239777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:58.876615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.473383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.399961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:10.046943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:13.720187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.438790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.271359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:25.369363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:07.994674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:13.009314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:16.902860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:21.007011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:25.590362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.453793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.201622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:37.056379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:40.688892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:44.327360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:48.085549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.875497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.409903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:59.043350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.637380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.569992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:10.225815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:13.959881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.610805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.440880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:25.530412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:08.210708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:13.178782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:17.056614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:21.179572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:25.750466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.632856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.364020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:37.235268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:40.856696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:44.499290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:48.244742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:52.028445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.564868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:59.205816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.804678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.732882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:10.395818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:14.124917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.788149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.601043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:25.702816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:09.375089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:13.353973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:17.238746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:21.370426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:26.125332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.817647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.544585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:37.414288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:41.035524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:44.676705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:48.426006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:52.196491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.745176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:59.387048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.986620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.913447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:10.579553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:14.310691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.974051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.773483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:25.861372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:09.538314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:13.523257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:17.401251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:21.639473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:26.284503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:29.989055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.702542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:37.583913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:41.192766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:44.843418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:48.577489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:52.354724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:55.911144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:59.537313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:03.141124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:07.067968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:10.747433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:14.479676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:18.143545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:21.936552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:26.047031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:09.737861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:13.734990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:17.640265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:21.863930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:26.482501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:30.185292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:33.890347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:37.771117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:41.387744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:45.039187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:49.058910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:52.542642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:56.093767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:59.723933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-17T11:54:31.954703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:35.607467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:39.470494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.107634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:46.800361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:50.709210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:54.207957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:57.789663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:01.421879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:05.355915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:08.965066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:12.658255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:16.406685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:20.222010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:23.773428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:27.875789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:11.859477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:15.729863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:19.810410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:24.452256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:28.420670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:32.133605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:35.776063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:39.637676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.284193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:46.981130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:50.876695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:54.384983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:57.962607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:01.590256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:05.534198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:09.144485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:12.825427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:16.570753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:20.402426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:23.935515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:28.043488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:12.073744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:15.906111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:20.028859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:24.625512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:28.581874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:32.310414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:35.947917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:39.805137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.456605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:47.150422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.044062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:54.547459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:58.129777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:01.769280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:05.701719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:09.335843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:12.993988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:16.738177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:20.566886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:24.100271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:28.225677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:12.278282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:16.087877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:20.211052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:24.814435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:28.756680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:32.500161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:36.133122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:39.985679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.634185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:47.337934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.214164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:54.726090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:58.312025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:01.952017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:05.885214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:09.522260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:13.182547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:16.914433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:20.753362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:24.707236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:28.377770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:12.463456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:16.243535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:20.395509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:24.980793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:28.915230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:32.667563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:36.292351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:40.141308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:43.791526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:47.562169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:51.377165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:54.881993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:54:58.521142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:02.111007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:06.045191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:09.682106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:13.355707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:17.075074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:20.923024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T11:55:24.855616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-17T11:55:38.540783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AñoAccesos por cada 100 hogaresBanda ancha fijaDial upTotal_BA_PADSLCablemodemFibra ópticaWirelessOtros_tecno_PTotal_tecno_PMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rango_PTotal_velo_rango_PTrimestreProvincia
Año1.0000.4600.286-0.2010.284-0.1170.4060.4870.4580.3690.2840.8680.310-0.008-0.0900.1420.1520.3250.6910.7400.2860.1330.000
Accesos por cada 100 hogares0.4601.0000.6110.4210.6130.2410.6860.6250.5440.5550.6130.4800.1820.1580.4760.3800.3270.2870.6130.4440.6100.0000.542
Banda ancha fija0.2860.6111.0000.2911.0000.8190.8330.6800.5480.5411.0000.5020.2060.2150.7940.6850.5500.3330.7290.3130.9990.0000.577
Dial up-0.2010.4210.2911.0000.2980.3000.1650.3930.3490.3840.298-0.239-0.0370.1420.5760.2360.105-0.106-0.004-0.0770.2990.0000.414
Total_BA_P0.2840.6131.0000.2981.0000.8190.8320.6820.5510.5431.0000.4990.2060.2150.7970.6860.5480.3300.7280.3120.9990.0000.620
ADSL-0.1170.2410.8190.3000.8191.0000.5570.4350.2250.3080.8190.1570.0580.1850.8310.6380.4500.1800.428-0.0320.8210.0000.486
Cablemodem0.4060.6860.8330.1650.8320.5571.0000.6770.5490.4690.8320.5640.1940.1830.6230.6010.5120.3980.8130.3810.8280.0000.500
Fibra óptica0.4870.6250.6800.3930.6820.4350.6771.0000.6700.6010.6820.5350.2890.2550.5250.5160.4300.1790.7510.4480.6840.0000.235
Wireless0.4580.5440.5480.3490.5510.2250.5490.6701.0000.5670.5510.4410.2160.0970.4430.3970.2930.1870.5500.4960.5480.0000.340
Otros_tecno_P0.3690.5550.5410.3840.5430.3080.4690.6010.5671.0000.5430.3880.2020.2180.3920.3370.2990.2300.5060.4940.5430.0000.347
Total_tecno_P0.2840.6131.0000.2981.0000.8190.8320.6820.5510.5431.0000.4990.2060.2150.7970.6860.5480.3300.7280.3120.9990.0000.620
Mbps (Media de bajada)0.8680.4800.502-0.2390.4990.1570.5640.5350.4410.3880.4991.0000.265-0.0220.0360.2970.3110.4140.8590.6920.4980.0000.198
Hasta 512 kbps0.3100.1820.206-0.0370.2060.0580.1940.2890.2160.2020.2060.2651.0000.0600.1080.1840.1240.1110.2200.2690.2140.0000.313
Entre 512 Kbps y 1 Mbps-0.0080.1580.2150.1420.2150.1850.1830.2550.0970.2180.215-0.0220.0601.0000.2240.1510.167-0.0350.0980.0500.2220.0000.270
Entre 1 Mbps y 6 Mbps-0.0900.4760.7940.5760.7970.8310.6230.5250.4430.3920.7970.0360.1080.2241.0000.6200.3710.1570.378-0.0050.7990.0000.408
Entre 6 Mbps y 10 Mbps0.1420.3800.6850.2360.6860.6380.6010.5160.3970.3370.6860.2970.1840.1510.6201.0000.4840.3060.4960.1330.6820.0000.317
Entre 10 Mbps y 20 Mbps0.1520.3270.5500.1050.5480.4500.5120.4300.2930.2990.5480.3110.1240.1670.3710.4841.0000.2310.4500.2000.5450.0000.209
Entre 20 Mbps y 30 Mbps0.3250.2870.333-0.1060.3300.1800.3980.1790.1870.2300.3300.4140.111-0.0350.1570.3060.2311.0000.4100.2660.3220.0000.191
Más de 30 Mbps0.6910.6130.729-0.0040.7280.4280.8130.7510.5500.5060.7280.8590.2200.0980.3780.4960.4500.4101.0000.5640.7250.0000.241
Otros_velo_rango_P0.7400.4440.313-0.0770.312-0.0320.3810.4480.4960.4940.3120.6920.2690.050-0.0050.1330.2000.2660.5641.0000.3130.0000.153
Total_velo_rango_P0.2860.6100.9990.2990.9990.8210.8280.6840.5480.5430.9990.4980.2140.2220.7990.6820.5450.3220.7250.3131.0000.0000.576
Trimestre0.1330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
Provincia0.0000.5420.5770.4140.6200.4860.5000.2350.3400.3470.6200.1980.3130.2700.4080.3170.2090.1910.2410.1530.5760.0001.000

Missing values

2023-07-17T11:55:28.649708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T11:55:29.141790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestreProvinciaAccesos por cada 100 hogaresBanda ancha fijaDial upTotal_BA_PADSLCablemodemFibra ópticaWirelessOtros_tecno_PTotal_tecno_PMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rango_PTotal_velo_rango_P
020221Buenos Aires76.084549131629345554243920452728865124212112552166872455542463.031591.030056.0313382.0321756.0290127.0161183.03381049.026280.04555424.0
120221Capital Federal111.80141537821631417541102290121722562308578429934141754188.0527000.05575.039918.077390.061053.043289.01188072.01717.01417541.0
220221Catamarca58.2162377162378141811551729118132822346237851.071000.0456000.04386.07009.08773.03761.035715.02207.062378.0
320221Chaco44.0614411451441193239164546366628164235614411946.0461000.01099.016888.021235.020898.013012.062946.07580.0144119.0
420221Chubut88.0517070192717162844133666679184305922105217162813.0113000.01677.061369.031856.033080.013871.014055.015607.0171628.0
520221Córdoba85.761003729741003803195610501799235838608439713100380350.0100000.012782.0165922.0126009.073967.034892.0577027.013104.01003803.0
620221Corrientes47.6414257441425784046279093108756774537414257838.067000.04029.024563.025665.027077.010452.043631.07094.0142578.0
720221Entre Ríos63.70267904222679266715913898623091260981259226792636.0107000.05745.050075.049620.042294.021578.081757.016750.0267926.0
820221Formosa34.9654548054548163971592751461687620205454830.097000.0448000.024113.06945.06613.0716000.015028.0588000.054548.0
920221Jujuy58.0611650901165092130760616259494662397511650928.058000.01761.022141.016321.037923.0576000.035287.02442.0116509.0
AñoTrimestreProvinciaAccesos por cada 100 hogaresBanda ancha fijaDial upTotal_BA_PADSLCablemodemFibra ópticaWirelessOtros_tecno_PTotal_tecno_PMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rango_PTotal_velo_rango_P
78220141Neuquén49.79880772303903804779028161997103813051903804.04133.0987000.077148.084000.01582.02000.022.00.083958.0
78320141Río Negro44.9193909115795066648862415687635761572950663.04670.04618.084304.073000.01062.01000.08.00.094736.0
78420141Salta28.7091265259129073131175384585680912903.053000.019677.064061.07192.0314000.00.00.00.091297.0
78520141San Juan27.605054375551298481614347228081173512983.0531000.02000.051056.00.00.00.00.00.051589.0
78620141San Luis9.35121933641255711306428354214480125574.07000.03000.012544.00.01000.00.02.00.012557.0
78720141Santa Cruz28.9426304460267641892710038140923264267643.0161000.01625.024972.01000.01000.00.00.00.026760.0
78820141Santa Fe47.855060006125066123226621742963059595164405066123.08456.0124468.0345225.020328.06845.023000.0668.00.0506013.0
78920141Santiago Del Estero16.313712493713332567359819915340371333.01234.010531.022817.02422.0109000.00.00.00.037113.0
79020141Tierra Del Fuego63.97272727662803821618283764812934280383.012000.0607000.030902.06000.00.00.00.00.031527.0
79120141Tucumán33.421299448813003212971783121139801300323.06000.034672.083210.011779.0362000.03000.00.00.0130032.0